A dimensionality reduction tool using gene detection pattern to mitigate noisy expression profile of scRNA-seq

Documentation for package ‘scBFA’ version 1.6.0

BinaryPCA	Performs Binary PCA (as outlined in our paper). This function take the input of gene expression profile and perform PCA on gene detection pattern
celltype	Cell types as labels of example scRNA-seq dataset(exprdata)
celltype_toy	toy cell type vector with 3 cell types generated for 5 cells in toy dataset
diagnose	Perform diagnoisis of dispersion on the expression profile to check whether scBFA works on specific dataset
disperPlot	Reference dataset(disperPlot)
exprdata	scRNA-seq dataset(exprdata)
getGeneExpr	Function to extract gene expression matrix from input observation matrix
getLoading	Function to get low dimensional loading matrix
getScore	Function to get low dimensional embedding matrix
gradient	Calculate gradient of the negative log likelihood, used for calls to the optim() function.
gradient_chunk	Calculate gradient of the negative log likelihood, used for calls to the optim() function.
scBFA	Perform BFA model on the expression profile
scNoiseSim	simulation to generate scRNA-seq data with varying level of gene detection noise versus gene count noise
zinb_toy	example zinb object after fitting a toy dataset with 5 cells and 10 genes